EMOTIONAL INTELLIGENCE AND SERVICE QUALITY OF FACILITATORS’ INDONESIA HUMAN RESOURCES DEVELOPMENT AGENCY (HRDA)
Bibliographic record
Abstract
The model which was widely known and describes the concept of service quality is the Service Quality (SERVQUAL) model proposed by Parasuraman et al (1985). However, this model has a limitation, because its application merely for service providers in the business sector, not for service providers in the public sector and service providers in the education sector. In education sector, facilitators are always involved in interpersonal interaction with the training participants. Some researchers agree to uncover the relationship between the emotional intelligence of service providers and service quality. Based on the literature review, there are limited studies in the field of education and training of the Civil Service Apparatus, especially regarding the relationship between emotional intelligence and service quality. Thus, this study aims to reveal the effect of facilitators’ emotional intelligence on service quality with respondents from participants of Basic Education and Training (Diklatsar), Leadership Education and Training 3 (Diklatpim 3), and Leadership and Education Training 4 (Diklatpim 4) at HRDA Province. This study uses quantitative methods. The sample size in this study was 462 people who were collected through a survey with a purposive sampling technique. The data analysis technique used is SEM through a two-stage approach. The results showed that the facilitators’ emotional intelligence of HRDA of Central Java, East Java, West Java, Jakarta, Banten, Central Sulawesi, and North Sumatera Provinces, had a significant positive effect on the quality of service with social awareness as the indicator with the highest effect.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".